oclaw/openclaw/extensions/open-prose/skills/prose/examples/40-rlm-self-refine.prose
oliver dbbe3add6a 重构主控编排与运行时预热链路,统一工作区提示词/专家调度协议并补齐 wiki 记忆注入与写回闭环。
同时收敛启动与运维脚本默认行为(含 wiki worker)、更新 Admin 可观测性与相关测试,降低首轮时延并提高运行稳定性。

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2026-04-26 08:34:33 +08:00

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# RLM: Self-Refinement
# Recursive improvement until quality threshold
input artifact: "The artifact to refine"
input criteria: "Quality criteria"
agent evaluator:
model: sonnet
prompt: "Score 0-100 against criteria. List specific issues."
agent refiner:
model: opus
prompt: "Make targeted improvements. Preserve what works."
block refine(content, depth):
if depth <= 0:
output content
let eval = session: evaluator
prompt: "Evaluate against: {criteria}"
context: content
if **score >= 85**:
output content
let improved = session: refiner
prompt: "Fix the identified issues"
context: { artifact: content, evaluation: eval }
output do refine(improved, depth - 1)
output result = do refine(artifact, 5)